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How Big Data is Revolutionizing Healthcare

The advent of technology and the emergence of Big Data have changed the way people or businesses leverage, analyse, and manage data across different industries. It is identified that over the past few years, Big Data adoption has increased in the global healthcare industry making efficient improvisation in the healthcare practice. Thus, the subsequent section is going to provide a detailed evaluation of the contribution of Big Data in healthcare in relation to health status prediction. Benefits and challenges associated with the adoption of Big Data technologies in health care along with application of Big Data in status prediction will be underlined by considering real-life case evidence.

The advancement of technology has changed and improvised business operations that enhance the chance for business entities across any industry to improve their efficiency, growth, and market competitiveness. As mentioned by Hong et al. (2018), in healthcare, transformation to digitalise records as well as rapid improvisation in the emerging medical technologies are found to pave the way for the adoption of big data in the healthcare field. Most of business industries have already adopted Big Data to manage and store vast amounts of data sets accurately that cannot be obtained with the usage of traditional Information Storage Systems (ISS).

Ristevski and Chen (2018) highlight that in order to offer best care and services to the patients, healthcare organisations are found to introduce or adopt different types of healthcare information systems including Electronic health records (EHRs) that helps to store and manage patient’s data in an accurate and systematic manner. It is identified that in healthcare, a wide range of datasets is found to be generated in a semi-structured or unstructured and structured format. Big Data in this regard plays a significant role in terms of managing and analysing these large volumes of complex data sets to develop valuable insight for healthcare organisations. 

It is identified that Big Data in healthcare supports in analysing large amounts of data sets from large volumes of patient information, perceiving clusters as well as correlation between the datasets. Big Data also provides healthcare professionals, with predictive models, with the help of a data mining approach. According to Senthilkumar et al. (2018), Big Data is found to be associated with five key characteristics such as volume, velocity, variety, veracity, and value based on which a large amount of data sets is being analysed, monitored, and stored. 

Big Data is found to gain popularity in the healthcare industry in terms of determining and indicating large amounts of identified or collected datasets. In the context of healthcare, the volume of data is found to be increasing at a rapid pace. It is seen that 30% of the data volume across the world is found to be generated from the healthcare industry. Along with this, it is expected that by 2025, this data volume will be increased at a CGAR rate of 36%, which is found to be a 6% increased growth from the other business industries (RBCCM, 2022). 

Velocity is found to be a more important characteristic of Big Data as compared to Volume as the rate at which a large volume of data set is being analysed is found to be directly influences the healthcare decision-making process (Müller et al. 2018).

Characteristics of Big Data in Healthcare

The global healthcare system is found to be associated with a large number of datasets obtained from different types of data sources and in different formats such as semi-structured, unstructured, and structured.

Data generated from the healthcare industry are found to be different in terms of reliability and quality. This signifies that having effective and relevant data supports healthcare organisations or professionals to make optimal decisions.

As mentioned earlier, in the healthcare industry large amounts of data sets are being generated, which may not be able to be managed and analysed appropriately with the help of traditional information systems (IS). This characteristic of Big Data helps businesses to transform large volumes of datasets into valuable insights based on which healthcare professionals can provide appropriate and required treatment to the respective patients.

Saranya and Asha (2019) mentioned that the application of Big Data helps healthcare institutions to make predictive analyses about patients' health status in real-time that positively influence them to provide appropriate and accurate services. The author justified this statement by highlighting that 79% of the US healthcare providers believe that Big Data application helps them to minimise medical errors followed by 52% of the healthcare providers who believe Big Data assist them to obtain a clear insight into patient's health status in accordance to which they are able to provide personalised care facilities. Rong et al. (2020) on the other hand highlights each of the characteristics associated with Big Data that helps healthcare professionals to perform predictive analysis. This, in turn, allows them to quickly integrate data as well as helps to plan specific treatment courses that will be appropriate for the respective patient and improve overall health care services.

The application or adoption of Big Data in healthcare is found to be associated with benefits and drawbacks, which are being underlined in the next adjacent sections.

In this present digital era, every business industry has been taking the advantage of digital technologies to ease operational activities and improve organisational performance. The adoption and implementation of Big Data in the healthcare industry are found to have a significant impact as compared to other industries. The key reason behind this statement is that it deals with medical data such as a patient's record, treatment schedule, diagnosis information, imagining results, and others (Aceto et al. 2020). Incorporating Big Data has entirely changed the way healthcare professionals and doctors analyse, utilise, and manage vast amounts of critical and complex medical data sets. In this regard, the demands for Big Data technologies have been increased as it allows healthcare professionals to improve treatment facilities and enhance the quality of a patient’s life in an appropriate manner (Wang et al. 2018). 

Key sources of Big Data technologies in healthcare are EHRs, wearable devices, patient portals, and others (Vyslotskyi, 2020). However, the adoption of Big Data is found to bring both benefits and drawbacks to health care professionals. The key reason behind this statement is justified in the next adjacent paragraph. 

The adoption of Big Data provides clear clinical insights to healthcare providers that positively influence the improvement of patient care facilities. Cutting-edge analytics in Big Data technologies allows healthcare providers to provide accurate and appropriate treatment facilities. It also maximises the chance for healthcare professionals to make optimal clinical decisions that minimise the occurrence of any kind of treatment ambiguity. Along with this, Big Data technologies and algorithms help to identify the accurate and best treatment for specific patients that improves overall patient care facilities.

Benefits and Challenges of Using Big Data in Healthcare

Every patient is found to have different healthcare needs, treatments, and diseases that are recorded in electronic health records (EHRs). Big Data in this regard helps to analyse those different vast data to make an accurate diagnosis and assist healthcare professionals to predict a patient's health status based on previous and existing illnesses along with diagnostic results.

Application of Big Data helps doctors and healthcare professionals to offer real-time services to their respective patients. The key reason behind this statement is that with the help of wearable devices, patients' health-related information is stored in the cloud. This, in turn, assists the doctors easily in real-time and helps doctors to prescribe appropriate medicines based on the real-time values and results. 

In order to generate trustworthy insight, Machine Learning, and Artificial Intelligence algorithms require appropriate data input with any kind of inaccuracy and duplication. For example, in case of inappropriate quality of data received, then doctors may fail to perform appropriate diagnoses that lead to the wrong treatment.

High visibility is found to be identified in the context of application of Big Data in healthcare. In this regard, operational dashboards, real-time monitoring, periodic business reports, and others are essential. However, due to the persistent of lack of specific expertise and tools for data visualisation, thus, it might not be executed appropriately in healthcare organisations.

Security and privacy are found to be one of the topmost concerns in the healthcare industry due to the lack of security management infrastructure. It is identified that the healthcare sector is most vulnerable to data theft, cyber-attack, and data breach issues (Sarma, 2020). Due to lack of regular security audits, incorporation of best security measures, lack of training for the employees and others creates challenges for healthcare organisations to store their data accurately after implementing Big Data technologies.

Operating Big Data technologies in healthcare requires highly skilled professionals as analysing, collecting, and managing vast amounts of data is found to be challenging (Mujumdar and Vaidehi, 2019). However, in the healthcare industry, a lack of highly skilled data analytic experts has been identified that may hinder the chance for healthcare businesses to obtain success after implementing Big Data.

Considering the evidence, it can be mentioned that it is important for the healthcare industry to address these challenges and identify its resource capabilities before implementing Big Data technologies. In contrast, it is identified that considering the benefits of Big Data, it can be mentioned that it would be necessary for healthcare organisations to improve their business process so that they can adapt and gain success through Big Data technologies (Lv and Qiao). This is because Big Data makes it easier for doctors and the healthcare profession to generate accurate results for any kind of severe disease that enhance the chance of improving existing healthcare practices to cure any acute life-threatening diseases.

With the rapid adoption of technology every business entity, specifically, healthcare institutions are found to show their concern towards adopting technology-based solutions to improve patient care (Nair et al. 2018). The concept of Big Data is found to be evolving across the healthcare industry by generating a significant impact on healthcare practices. As mentioned by (Habeeb et al. 2019) Big Data is found to offer a predictive solution that helps healthcare organisations to improve their services along with improving the quality of patients' health. Along with this, offering predictive and accurate insight from large amounts of data sets, Big Data helps healthcare professionals to make optimal clinical decisions in relation to the patient's treatment. In order to evaluate the application of Big Data in healthcare practices for analysing health status, the following three real-life case evidence has been considered.

Health Status Prediction using Big Data

In recent times, people, as well as healthcare professionals, have been struggling hard to minimise the spread of infectious Covid-19 disease. Data related to Covid-19 has been generated rapidly due to its changing symptoms, variants, and treatment pattern (Pham et al. 2020). It becomes difficult for healthcare professionals and doctors to manage and analyse these vast amounts of data with the help of traditional information systems and predict the probable future health status of patients.

In this regard, Alsunaidi et al. (2021) underline a case scenario where the author mentioned the way Big Data application helps to predict the health status of Covid-19 patients accurately based on which appropriate care facilities can be provided. 

The author has proposed an analytical model based on Machine Learning techniques along with Multilayer Perceptron (MLP) as well as Long Short-Term Memory (LSTM) to enhance the predictive analysis of the patient’s health condition. Along with this, the case scenario also highlights that by utilising wearable devices and health tracking systems, in the future; Big Data would help healthcare professionals to detect respiratory infections and health condition accurately based on which control measures for Covid-19 can be undertaken.

It is identified that over the past few years Alzheimer's disease is considered as one of the most critical and neurodegenerative disorders that negatively influence people's mental health and well-being (Noor et al. 2020). The growing rate of mentioned disease generates urgency for doctors and healthcare professionals to undertake an appropriate strategic approach or technology-based solution to detect the disease at an early stage so that they could prevent its severe consequences. In this regard, Sharma et al. (2019) underline a Hadoop-based framework namely “BHARAT” to detect Alzheimer at the early stage. 

It can be mentioned that the mentioned framework is being associated with four specific components such as Data Processing, Data Storage, Data Management, and Data Normalisation respectively. The author highlights the way the application of Big Data algorithms helps to process and analyse vast amounts of clinical data regarding Alzheimer disease in accordance to which they could make optimal decisions.

Diabetics are found to be one of the most threatening health diseases and it can be considered as a global burden. Despite existing different approaches for predicting diabetics, the application of Big Data is found to be rare and it is at the initial stage. In this regard, Suvarnamukhi and Seshashayee (2019) develop a diabetic prediction system by utilising MapReduce and Hadoop environments. The author mentioned that application of Big Data helps to maintain an effective operational flow throughout healthcare practices. The healthcare professionals in terms of managing and analysing patients' health reports, medical conditions, and their status in an accurate and systematic manner, which is stored in EHR in different formats. This, in turn, helps to predict the patent of diabetics among respective patient in accordance to which proper medication can be provided.

Conclusion

In the context of the above discussion, the application of Big Data in the healthcare industry is found to be growing at a rapid pace due to its modernised and significant features for generating accurate diagnosis results. In healthcare, Big Data is utilised to illustrate a wide range of data sets developed by the incorporation of technologies, which collects patient data and support to manage overall healthcare practices. From the discussion, it has been identified that the identified characteristics of Big Data help to analyse, manage, and store vast amounts of data sets in an accurate and systematic manner. On the other hand, it has been identified that the application of Big Data in healthcare provides a new way for healthcare professionals and doctors to improve their treatment and care services. Along with this, some identified disadvantages needs to be considered by healthcare organisations before implementing healthcare technologies like Big Data. Along with this, the mentioned case study provides a clear insight into the way Big Data and its associated algorithms along with analytical model helps to predict health status in an accurate and systematic manner.  

Real-life Cases

References

Aceto, G., Persico, V. and Pescapé, A., 2020. Industry 4.0 and health: Internet of things, big data, and cloud computing for healthcare 4.0. Journal of Industrial Information Integration, 18, p.100129.

Alsunaidi, S.J., Almuhaideb, A.M., Ibrahim, N.M., Shaikh, F.S., Alqudaihi, K.S., Alhaidari, F.A., Khan, I.U., Aslam, N. and Alshahrani, M.S., 2021. Applications of big data analytics to control COVID-19 pandemic. Sensors, 21(7), p.2282.

Habeeb, R.A.A., Nasaruddin, F., Gani, A., Hashem, I.A.T., Ahmed, E. and Imran, M., 2019. Real-time big data processing for anomaly detection: A survey. International Journal of Information Management, 45, pp.289-307.

Hong, L., Luo, M., Wang, R., Lu, P., Lu, W. and Lu, L., 2018. Big data in health care: Applications and challenges. Data and information management, 2(3), pp.175-197.

Lv, Z. and Qiao, L., 2020. Analysis of healthcare big data. Future Generation Computer Systems, 109, pp.103-110.

Mujumdar, A. and Vaidehi, V., 2019. Diabetes prediction using machine learning algorithms. Procedia Computer Science, 165, pp.292-299.

Müller, O., Fay, M. and Vom Brocke, J., 2018. The effect of big data and analytics on firm performance: An econometric analysis considering industry characteristics. Journal of Management Information Systems, 35(2), pp.488-509.

Nair, L.R., Shetty, S.D. and Shetty, S.D., 2018. Applying spark based machine learning model on streaming big data for health status prediction. Computers & Electrical Engineering, 65, pp.393-399.

Noor, M.B.T., Zenia, N.Z., Kaiser, M.S., Mamun, S.A. and Mahmud, M., 2020. Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease, Parkinson’s disease and schizophrenia. Brain informatics, 7(1), pp.1-21.

Pham, Q.V., Nguyen, D.C., Huynh-The, T., Hwang, W.J. and Pathirana, P.N., 2020. Artificial intelligence (AI) and big data for coronavirus (COVID-19) pandemic: A survey on the state-of-the-arts. IEEE access, 8, p.130820.

RBCCM, 2022. The healthcare data explosion. [Online]. Available at: <https://www.rbccm.com/en/gib/healthcare/episode/the_healthcare_data_explosion> [Accessed on 28 March 2022]

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Sarma, H.K.D., 2020. Security issues in big data. Trends in Communication, Cloud, and Big Data, pp.67-72.

Senthilkumar, S.A., Rai, B.K., Meshram, A.A., Gunasekaran, A. and Chandrakumarmangalam, S., 2018. Big data in healthcare management: a review of literature. American Journal of Theoretical and Applied Business, 4(2), pp.57-69.

Sharma, A., Shukla, D., Goel, T. and Mandal, P.K., 2019. BHARAT: an integrated big data analytic model for early diagnostic biomarker of Alzheimer's disease. Frontiers in neurology, p.9.

Suvarnamukhi, B. and Seshashayee, M., 2019. Big data processing system for diabetes prediction using machine learning technique. IJITEE, ISSN, pp.2278-3075.

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Wang, Y., Kung, L. and Byrd, T.A., 2018. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, pp.3-13. 

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